A rank-aware block decomposition for linear and bilinear operations in recommender models (FM, DCNv2, attention, FC) reduces redundant context feature computation to once per request with identity-equivalent results, plus rDCN variant for deeper layers.
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DS-MLP achieves state-of-the-art CTR prediction on three benchmarks using a final vanilla MLP structure trained via knowledge distillation and two alignment strategies.
MoS applies theme-aware routing to extract multi-scale theme-specific subsequences from noisy long user sequences, achieving state-of-the-art recommendation performance with fewer FLOPs than comparable MoE models.
citing papers explorer
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Dual-Stream MLP is All You Need for CTR Prediction
DS-MLP achieves state-of-the-art CTR prediction on three benchmarks using a final vanilla MLP structure trained via knowledge distillation and two alignment strategies.
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Mixture of Sequence: Theme-Aware Mixture-of-Experts for Long-Sequence Recommendation
MoS applies theme-aware routing to extract multi-scale theme-specific subsequences from noisy long user sequences, achieving state-of-the-art recommendation performance with fewer FLOPs than comparable MoE models.